Jeroen van Zundert
Jeroen van Zundert
I recently posted the groupby solution on StackOverflow on a similar question: https://stackoverflow.com/questions/74418401/how-to-use-polars-to-get-the-rolling-values-for-two-dataframes-col-by-col. Using `rolling_mean` and `rolling_var` would probably be faster though as mentioned.
The problem is the inconsistency in return type: ```python >>> type(np.exp(x)) >>> type(np.exp(x, out=y)) # does not work now, but under your proposal ``` We can add a check on...
I cannot reproduce this error. @kgguliev : your details suggest pyarrow is installed in the same session, so it is odd that pyarrow is not loaded properly according to the...
Two questions/remarks and one suggestion: First, note that the [current behavior](https://pola-rs.github.io/polars/py-polars/html/reference/expressions/api/polars.Expr.dt.week.html#polars.Expr.dt.week) for `week` is that it represents the week of the year (1-> 53). Are you suggesting to change that...
Hello, thank you for the report. I contributed `glimpse` in #5622 . First version had `print` embedded, but [it was decided to allow extra flexibility](https://github.com/pola-rs/polars/pull/5622#discussion_r1032062384) by returning just a string....
The options I could come up so far: 1. add a `return_as_string` parameter, default to `False` (otherwise annoying for interactive use). If `False`, print, if `True`, return as string 2....
Closing in favor of the more generic discussion about using unsigned integers in the Polars api in #4990. Please comment and/or upvote there.
I think `map_dict` is a better name, as it tells you that you are doing a `map`, with a dict as input, in contrast to the current `map` which takes...
So we have various `map` methods & functions currently: 1. `LazyFrame.map` => take in callable, return lazy frame 2. `Expr.map` => takes in callable, return expression 3. `pl.map` => like,...
If boolean, that would have to be two indicators, one for whether it occurs in the original left dataframe, and one for the right dataframe. I see Pandas opts for...